Evidence map›Paper›PMID 42232732›Full record

ArticleStatistics and its interface2026

Improving Overall Risk Ranking via Subgroup-Level Information Borrowing in Survival Risk Stratification.

Tia S Thomas, Jing Ning, Ruosha Li

Abstract read
In one paragraph

Article in Statistics and its interface, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Tia S ThomasUTHealth Houston School of Public Health, 1200 Pressler St., Houston, TX, USA.
Jing NingThe University of Texas MD Anderson Cancer Center, 1400 Pressler St., Houston, TX, USA.
Ruosha LiUTHealth Houston School of Public Health, 1200 Pressler St., Houston, TX, USA.

Funding

Center for Clinical and Translational SciencesUM1TR004906 · NCATS · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI Maria Eulalia Fernandez, LORNA H. MCNEILL · 2024 to 2026
$16.7M
The University of Texas MD Anderson Cancer Center SPORE in Ovarian CancerP50CA281701 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI ROBERT C BAST, ANIL K SOOD · 2023 to 2026
$11.5M
Consortium on Translational Research in Early Detection of Liver Cancer:Data Management and Coordinating Center (DMCC)U24CA230144 · NCI · FRED HUTCHINSON CANCER RESEARCH CENTER · PI Ziding Feng · 2018 to 2026
$5.8M
Statistical Methods for Integration of Multiple Data Sources toward Precision Cancer MedicineR01CA269696 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI JING NING, YU SHEN · 2022 to 2026
$1.7M
NCATS NIH HHS UM1 TR004906NCI NIH HHS P50 CA281701NCI NIH HHS R01 CA269696NCI NIH HHS U24 CA230144
6 · The paper itself

Abstract

Effective risk stratification is essential for providing tailored therapies, improving patient outcomes, and optimizing healthcare resources by identifying sub-populations with similar health risks. However, accurate risk ranking is challenging in the presence of heterogeneous subgroups. Under these instances, subgroup-level information can be leveraged to refine the overall risk ranking. We propose a novel approach that integrates within-subgroup risk ranking percentiles to enhance the overall cohort risk stratification. This method uses both a global model and subgroup-specific models along with optimized weights to improve discriminatory performance across the entire cohort. The proposed method is validated through extensive simulations and applied to a study of end-stage renal disease patients awaiting kidney transplantation.

Indexed as

Information borrowingRisk stratificationSubgroup heterogeneitySubgroup weighting

Identifiers

PMID42232732
PMCPMC13225772

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.